Analyse maisons - Shapash

Interpretation des predictions maisons

Project_Information

Author : VotreNom

Description : Rapport Shapash pour maisons

Project_Name : Analyse lightgbm_maison


Model analysis

Model used : LGBMRegressor

Library : lightgbm.sklearn

Library version : 4.6.0

Model parameters :

Parameter key Parameter value
boosting_type gbdt
objective None
num_leaves 43
max_depth 9
learning_rate 0.1269191174033963
n_estimators 176
subsample_for_bin 200000
min_split_gain 0.0
min_child_weight 0.001
min_child_samples 10
subsample 1.0
subsample_freq 0
colsample_bytree 1.0
reg_alpha 0.0
reg_lambda 0.0
random_state None
Parameter key Parameter value
n_jobs None
importance_type split
_Booster
_evals_result {}
_best_score defaultdict(, {})
_best_iteration 0
_other_params {}
_objective regression
class_weight None
_class_weight None
_class_map None
_n_features 55
_n_features_in 55
_classes None
_n_classes -1
fitted_ True

Dataset analysis

Global analysis

Training dataset Prediction dataset
number of features NaN 55
number of observations NaN 2,783
missing values NaN 0
% missing values NaN 0

Univariate analysis

etage - Categorical

Prediction dataset
distinct values 1
missing values 0

Target analysis

prix_m2_vente - Numeric

Prediction dataset
count 2,783
mean 2,710
std 951
min 150
25% 2,100
50% 2,700
75% 3,250
max 8,190

Multivariate analysis


Model explainability

Note : the explainability graphs were generated using the test set only.

Global feature importance plot

Features contribution plots

etage -


Model performance

Univariate analysis of target variable

prix_m2_vente - Numeric

True values Prediction values
count 2,783 2,783
mean 2,710 2,700
std 951 762
min 150 355
25% 2,100 2,220
50% 2,700 2,710
75% 3,250 3,180
max 8,190 6,340

Metrics

MAE : 402

R2 : 0.661

MSE : 306,000

MAPE : 0.172

MdAE : 305

Explained Variance : 0.661